{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading market data examples\n",
    "\n",
    "Saeed Amen - saeed@cuemacro.com\n",
    "\n",
    "Getting market data using findatapy for a number of different data sources. findatapy is used extensively in finmarketpy. The idea is that we use a common API for all these different data sources, which makes it easier to combine them together in our analysis. Here we'll show some examples of downloading data from `quandl` and `bloomberg`.\n",
    "\n",
    "findatapy also has ticker mapping functionality that allows you to use your own nicknames for tickers, rather than the vendor tickers. There are lots of in built ticker mappings already (defined in CSV files which you can edit in the config folder). Later we show, how to download our predefined ticker mappings and also how to query them, to extract the original vendor tickers. Most of the predefined tickers involve FX markets, given the focus of Cuemacro.\n",
    "\n",
    "Let's first do some imports for objects we'll use later from `chartpy`, `findatapy` and `finmarketpy`. We'll also disable the log output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:07.408175Z",
     "start_time": "2022-01-26T15:55:59.685770Z"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "from chartpy import Chart, Style\n",
    "from findatapy.market import Market, MarketDataGenerator, MarketDataRequest\n",
    "\n",
    "# So we don't see deprecated warnings... when you're coding it's usually good to leave these!\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Disable logging messages, to make output tidier\n",
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.disable(sys.maxsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the engine to output the chart. One of the engines we can use is `matplotlib` and `plotly` is also supported. Create the `Market` object for fetching data in conjunction with `MarketDataRequest` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:10.503206Z",
     "start_time": "2022-01-26T15:56:07.410175Z"
    }
   },
   "outputs": [],
   "source": [
    "chart = Chart(engine='matplotlib')\n",
    "market = Market(market_data_generator=MarketDataGenerator())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the MarketDataRequest?\n",
    "\n",
    "`MarketDataRequest` is used to request market data from a data vendor/external source. Below we mention some of the properties.\n",
    "\n",
    "`freq` represents the frequency of the data we want to download. Note that not every data vendor will provide high frequency data for every ticker.\n",
    "\n",
    "* `daily` - daily data (default)\n",
    "* `tick` - tick data (eg. `dukascopy`, `fxcm`, `bloomberg`, `eikon`)\n",
    "* `intraday` - minute data (eg. `bloomberg`, `eikon`)\n",
    "\n",
    "findatapy supports many different `data_source` from a number of different vendors (both traditional market data and cryptocurrencies). Here we write a selection. We are continually adding more sources. If you'd like to sponsor the addition of a new data source let us know!\n",
    "\n",
    "* `bloomberg` - Bloomberg terminal/blpapi (paid) \n",
    "* `eikon` - Refinitiv Eikon (paid)\n",
    "* `quandl` - Quandl has a mix of free and premium data\n",
    "* `dukascopy` - retail FX tick data (free) \n",
    "* `fxcm` - retail FX tick data (free)\n",
    "* `alfred` - ALFRED/FRED mostly economic data (free)\n",
    "* `yahoo` - equities data (free)\n",
    "* `bitcoincharts`, `poloniex`, `binance`, `bitfinex`, `gdax`, `kraken`, `bitmex`, `alphavantage`, `huobi` - crypto data\n",
    "\n",
    "Or  we can give `MarketDataRequest` a `csv` or `parquet` file as a source\n",
    "\n",
    "* `tickers`: (can be list) eg. `EURUSD` - our nickname for tickers to use internally in findatapy\n",
    "* `vendor_tickers`: (can be list) eg. `EURUSD Curncy` - the particular vendor ticker to use for that same asset\n",
    "* `fields`: (can be list) eg. `close` - our nickname for the field\n",
    "* `vendor_fields`: (can be list) eg. `PX_LAST` - the particular vendor field\n",
    "* `cache_algo` (eg. internet, memory) eg. `internet_load_return` - internet will forcibly download from the internet\n",
    "* `quandl_api_key`: insert API key from Quandl (freely available from their website)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading from Quandl example\n",
    "\n",
    "In the below example, we download the monthly average interest rate for UK resident monetary financial institutions, using Quandl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:13.371422Z",
     "start_time": "2022-01-26T15:56:13.231436Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import os\n",
    "    QUANDL_API_KEY = os.environ['QUANDL_API_KEY']\n",
    "except:\n",
    "    QUANDL_API_KEY = 'TYPE_YOUR_KEY_HERE'\n",
    "\n",
    "# Monthly average of UK resident monetary financial institutions' (excl. Central Bank) sterling\n",
    "# Weighted average interest rate, other loans, new advances, on a fixed rate to private non-financial corporations (in percent)\n",
    "# not seasonally adjusted\n",
    "md_request = MarketDataRequest(\n",
    "    start_date=\"01 Jan 2005\",  # start date\n",
    "    data_source='quandl',      # use Quandl as data source\n",
    "    tickers=['Weighted interest rate'],\n",
    "    fields=['close'],         # which fields to download\n",
    "    vendor_tickers=['BOE/CFMBJ84'],  # ticker (Bloomberg)\n",
    "    vendor_fields=['close'],  # which Bloomberg fields to download\n",
    "    cache_algo='internet_load_return',\n",
    "    quandl_api_key=QUANDL_API_KEY)  # how to return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've defined the `MarketDataRequest` and now we can download it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:15.548520Z",
     "start_time": "2022-01-26T15:56:14.242467Z"
    }
   },
   "outputs": [],
   "source": [
    "df = market.fetch_market(md_request)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set style for graph, axis labels and plot. Creates an object with essentially the same characteristics as a matplotlib graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:16.404113Z",
     "start_time": "2022-01-26T15:56:16.384105Z"
    }
   },
   "outputs": [],
   "source": [
    "style = Style()\n",
    "\n",
    "style.title = 'BoE weighted interest rate'\n",
    "style.scale_factor = 3\n",
    "style.file_output = \"boe-rate.png\"\n",
    "style.source = 'Quandl/BoE'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:17.800925Z",
     "start_time": "2022-01-26T15:56:17.350447Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart.plot(df, style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using strings to make data requests\n",
    "\n",
    "We noted that we have predefined tickers already in our ticker mapping framework. We also showed how to use `MarketDataRequest` to download market data, whether it is predefined or not. Here we are downloading EURUSD data from Bloomberg with NY close, but rather than using a `MarketDataRequest` we just use a single string, which obviously has a lot less boilerplate code. By default it will select one week. Note for this section to work, you will need to run this on a machine with `blpapi` installed, and a Bloomberg Terminal subscription on there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T16:14:20.356515Z",
     "start_time": "2022-01-26T16:14:18.664996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            EURUSD.close\n",
      "Date                    \n",
      "2021-01-27        1.2111\n",
      "2021-01-28        1.2122\n",
      "2021-01-29        1.2136\n",
      "2021-02-01        1.2060\n",
      "2021-02-02        1.2044\n",
      "...                  ...\n",
      "2022-01-20        1.1312\n",
      "2022-01-21        1.1344\n",
      "2022-01-24        1.1326\n",
      "2022-01-25        1.1301\n",
      "2022-01-26        1.1285\n",
      "\n",
      "[261 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(\"fx.bloomberg.daily.NYC.EURUSD.close\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download intraday EURUSD data in the same way, which is a predefined ticker (underneath this will map to `vendor_tickers` for Bloomberg `EURUSD CMPN Curncy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:24.329239Z",
     "start_time": "2022-01-26T15:56:22.005440Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           EURUSD.close\n",
      "Date                                   \n",
      "2022-01-19 15:57:00+00:00        1.1344\n",
      "2022-01-19 15:58:00+00:00        1.1345\n",
      "2022-01-19 15:59:00+00:00        1.1345\n",
      "2022-01-19 16:00:00+00:00        1.1347\n",
      "2022-01-19 16:01:00+00:00        1.1345\n",
      "...                                 ...\n",
      "2022-01-26 15:52:00+00:00        1.1282\n",
      "2022-01-26 15:53:00+00:00        1.1281\n",
      "2022-01-26 15:54:00+00:00        1.1281\n",
      "2022-01-26 15:55:00+00:00        1.1281\n",
      "2022-01-26 15:56:00+00:00        1.1282\n",
      "\n",
      "[7200 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(\"fx.bloomberg.intraday.NYC.EURUSD.close\", start_date='week')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can download arbitary tickers, which are not predefined too in our CSV files, using the `raw` keyword, and select whatever tickers we want. We haven't specified all the parameters like `freq` or `fields`, in which case the defaults we use. Here it would be `daily` and `close` on Bloomberg which corresponds to `PX_LAST`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-26T15:56:28.474360Z",
     "start_time": "2022-01-26T15:56:26.101164Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            VIX.close\n",
      "Date                 \n",
      "2022-01-20  25.590000\n",
      "2022-01-21  28.850000\n",
      "2022-01-24  29.900000\n",
      "2022-01-25  31.160000\n",
      "2022-01-26  29.700001\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(\"raw.data_source.bloomberg.tickers.VIX.vendor_tickers.VIX Index\", start_date='week')\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying for predefined tickers\n",
    "\n",
    "We can also download all the tickers in our predefined tickers list (which are defined in our CSV files), which has this format, and will likely contain many tickers.\n",
    "\n",
    "`category.data_source.freq.cut`\n",
    "\n",
    "We can also specify individual predefined tickers and fields as follows:\n",
    "\n",
    "`category.data_source.freq.cut.tickers.fields`\n",
    "\n",
    "Let's get all the predefined tickers/categories, which are from `quandl` and a few from `bloomberg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T17:05:47.233735Z",
     "start_time": "2021-05-04T17:05:47.220735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fx.quandl.daily.NYC', 'fx-spot-volume.quandl.daily.LOC']\n",
      "['fx.bloomberg.daily.BGN', 'fx.bloomberg.daily.BSTP', 'fx.bloomberg.daily.NYC', 'fx.bloomberg.daily.TOK', 'fx.bloomberg.daily.LDN', 'fx.bloomberg.daily.10AM', 'fx-tot.bloomberg.daily.NYC', 'fx-tot-forwards.bloomberg.daily.NYC', 'fx.bloomberg.intraday.NYC', 'fx.bloomberg.intraday.BGNE']\n"
     ]
    }
   ],
   "source": [
    "from findatapy.util import ConfigManager\n",
    "cm = ConfigManager().get_instance()\n",
    "\n",
    "# Get all the categories for raw data (note this won't include generated categories like fx-vol-market,\n",
    "# which aggregate from many other categories)\n",
    "categories = list(cm.get_categories_from_tickers())\n",
    "\n",
    "# Filter those categories which include quandl\n",
    "quandl_category = [x for x in categories if 'quandl' in x]\n",
    "print(quandl_category)\n",
    "\n",
    "# Filter those categories which include bloomberg and print the first few\n",
    "bloomberg_category = [x for x in categories if 'bloomberg' in x][0:10]\n",
    "print(bloomberg_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download all the tickers in the category/data_source/freq/cut for `fx.quandl.daily.NYC`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T16:36:59.346197Z",
     "start_time": "2021-05-04T16:36:58.401542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            EURUSD.close  GBPUSD.close  AUDUSD.close  NZDUSD.close  \\\n",
      "Date                                                                 \n",
      "2020-05-05        1.0844        1.2449        0.6458        0.6065   \n",
      "2020-05-06        1.0806        1.2347        0.6414        0.6019   \n",
      "2020-05-07        1.0815        1.2349        0.6500        0.6086   \n",
      "2020-05-08        1.0854        1.2436        0.6543        0.6147   \n",
      "2020-05-11        1.0818        1.2330        0.6472        0.6065   \n",
      "\n",
      "            USDCAD.close  USDCHF.close  USDNOK.close  USDSEK.close  \\\n",
      "Date                                                                 \n",
      "2020-05-05        1.4025        0.9721       10.2603        9.8296   \n",
      "2020-05-06        1.4143        0.9745       10.2799        9.8486   \n",
      "2020-05-07        1.3972        0.9746       10.2274        9.8128   \n",
      "2020-05-08        1.3910        0.9699       10.1864        9.7590   \n",
      "2020-05-11        1.4024        0.9722       10.2850        9.8649   \n",
      "\n",
      "            USDJPY.close  \n",
      "Date                      \n",
      "2020-05-05    106.519997  \n",
      "2020-05-06    106.070000  \n",
      "2020-05-07    106.360001  \n",
      "2020-05-08    106.500000  \n",
      "2020-05-11    107.699997  \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import os\n",
    "    QUANDL_API_KEY = os.environ['QUANDL_API_KEY']\n",
    "except:\n",
    "    QUANDL_API_KEY = 'TYPE_YOUR_KEY_HERE'\n",
    "\n",
    "df = market.fetch_market(md_request_str=\"fx.quandl.daily.NYC\", start_date='year',\n",
    "                         md_request=MarketDataRequest(quandl_api_key=QUANDL_API_KEY))\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also query this category combination too for all available tickers/fields/vendor tickers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T16:41:51.164251Z",
     "start_time": "2021-05-04T16:41:51.149255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For category fx.quandl.daily.NYC\n",
      "tickers = ['EURUSD', 'GBPUSD', 'AUDUSD', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDNOK', 'USDSEK', 'USDJPY']\n",
      "vendor_tickers = ['FRED/DEXUSEU', 'FRED/DEXUSUK', 'FRED/DEXUSAL', 'FRED/DEXUSNZ', 'FRED/DEXCAUS', 'FRED/DEXSZUS', 'FRED/DEXNOUS', 'FRED/DEXSDUS', 'FRED/DEXJPUS']\n",
      "fields = ['close']\n"
     ]
    }
   ],
   "source": [
    "# For this category, get all the tickers, vendor_tickers and fields which are available\n",
    "tickers = cm.get_tickers_list_for_category_str(categories[0])\n",
    "fields = cm.get_fields_list_for_category_str(categories[0])\n",
    "vendor_tickers = cm.get_vendor_tickers_list_for_category_str(\"fx.quandl.daily.NYC\")\n",
    "\n",
    "# We don't need to add the environment (eg. backtest)\n",
    "print(\"For category \" + quandl_category[0])\n",
    "print(\"tickers = \" + str(tickers))\n",
    "print(\"vendor_tickers = \" + str(vendor_tickers))\n",
    "print(\"fields = \" + str(fields))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download EURUSD from Quandl, using our predefined mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T16:36:59.692074Z",
     "start_time": "2021-05-04T16:36:59.363195Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            EURUSD.close\n",
      "Date                    \n",
      "2020-05-05        1.0844\n",
      "2020-05-06        1.0806\n",
      "2020-05-07        1.0815\n",
      "2020-05-08        1.0854\n",
      "2020-05-11        1.0818\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(md_request_str=\"fx.quandl.daily.NYC.EURUSD\", start_date='year',\n",
    "                         md_request=MarketDataRequest(quandl_api_key=QUANDL_API_KEY))\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract the `vendor_tickers` for our predefined ticker, not just for large categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T16:42:00.631540Z",
     "start_time": "2021-05-04T16:42:00.622540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED/DEXUSEU\n"
     ]
    }
   ],
   "source": [
    "vendor_tickers = cm.convert_library_to_vendor_ticker_str(\"fx.quandl.daily.NYC.EURUSD\")\n",
    "\n",
    "print(vendor_tickers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert from our predefined fields to the vendor field. Here we show that our `close` field for `bloomberg` is `PX_LAST` on Bloomberg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-04T16:42:34.354010Z",
     "start_time": "2021-05-04T16:42:34.348012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PX_LAST\n"
     ]
    }
   ],
   "source": [
    "vendor_fields = cm.convert_library_to_vendor_field('bloomberg', 'close')\n",
    "print(vendor_fields)"
   ]
  }
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